SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1631–1640 of 3304 papers

TitleStatusHype
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—0
Non-PSD Matrix Sketching with Applications to Regression and Optimization—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery—0
Computer-aided Interpretable Features for Leaf Image ClassificationCode0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Quantum diffusion map for nonlinear dimensionality reduction—0
Distributionally Robust Optimization with Markovian DataCode0
Quantifying the Conceptual Error in Dimensionality Reduction—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified